A design science approach for web service discovery

The Internet space is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to- Application communication, Web services can be combined with component-based software development to promote application interaction and integration within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web services repositories not only be well-structured but also provide efficient tools for an environment supporting reusable software components for both service providers and consumers. As the potential of Web services for service-oriented computing is becoming widely recognized, the demand for an integrated framework that facilitates service discovery and publishing is concomitantly growing. In this paper, we propose a design science approach for Web service discovery that combines a clustering technique with string/document matching methods and leverage the semantics of the XML-based service specification in WSDL files. We believe that this is one of the first attempts to apply an artificial neural network-based data mining technique in the Web service discovery domain. We have developed a Web service similarity assessment tool by using an artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from public Web services repositories.